Name
Title | ||
|---|---|---|
Real-time sufficient dimension reduction through principal least squares support vector machines |
Abstract | ||
|---|---|---|
•First approach to real time SVM-based sufficient dimension (SDR).•Computationally very fast and more accurate SDR than other methods.•It allows the real-time update by adding data or deleting old data. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2020.107768 | Pattern Recognition |
Keywords | DocType | Volume |
Central subspace,Ladle estimator,Online sliced inverse regression,Principal support vector machines,Streamed data | Journal | 112 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Artemiou | 1 | 2 | 3.15 |
| Yuexiao Dong | 2 | 3 | 4.67 |
| Seungjun Shin | 3 | 23 | 3.82 |